Accessing BCO-DMO data
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| Dataset Title: | [IODP360 - FPKM values] - Supplementary Table 4A: Metatranscriptome data summary for cellular activities presented and statistics on sequencing and removal of potential contaminant sequences, FPKM values (Collaborative Research: Delineating The Microbial Diversity and Cross-domain Interactions in The Uncharted Subseafloor Lower Crust Using Meta-omics and Culturing Approaches) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_812936) |
| Information: | Summary
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Attributes {
s {
Cycle {
String bcodmo_name "unknown";
String description "Cycle of the biosynthetic pathway";
String long_name "Cycle";
String units "unitless";
}
Biosynthetic_pathway {
String bcodmo_name "unknown";
String description "Name of biosynthetic pathway";
String long_name "Biosynthetic Pathway";
String units "unitless";
}
ID_2R1 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 540.183;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 2R1";
String long_name "ID 2 R1";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_19R1 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 86.455;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 19R1";
String long_name "ID 19 R1";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_26R2 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 968.764;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 26R2";
String long_name "ID 26 R2";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_31R1 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 256.836;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 31R1";
String long_name "ID 31 R1";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_42R2 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 1003.3;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 42R2";
String long_name "ID 42 R2";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_51R3 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 3001.126;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 51R3";
String long_name "ID 51 R3";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_62R1 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 2625.771;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 62R1";
String long_name "ID 62 R1";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_68R4 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 1097.931;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 68R4";
String long_name "ID 68 R4";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_71R1 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 500.262;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 71R1";
String long_name "ID 71 R1";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_81R2 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 163524.0;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 81R2";
String long_name "ID 81 R2";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
ID_84R6 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 352.436;
String bcodmo_name "unknown";
String description "FPKM values per pathway for sample 84R6";
String long_name "ID 84 R6";
String units "Fragments per Kilobase of transcript per Million mapped reads (FPKM)";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
String acquisition_description
"Frozen rock material was crushed as above, and then ground quickly into a fine
powder using a precooled sterilized mortar and pestle, and then RNA extraction
started immediately. The jaw crusher was cleaned and rinsed with 70% ethanol
and RNaseZap\\u2122 RNase Decontamination Solution (Invitrogen, USA) between
samples. About 40 g of material was extracted for each sample using the RNeasy
PowerSoil Total RNA Isolation Kit (Qiagen, USA) according to the
manufacturer\\u2019s protocol with the following modifications.
Each sample was evenly divided into 8 Bead Tubes (Qiagen, USA) and then 2.5 mL
of Bead Solution were added into the Bead Tube followed by 0.25 mL of Solution
SR1 and 0.8 mL of Solution SR2. Bead Tubes were frozen in liquid nitrogen and
then thawed at 65\\u00b0C in a water bath three times. RNA was purified using
the MEGAclear Transcription Clean-up Kit (Ambion, USA) and concentrated with
an overnight isopropanol precipitation at 4 \\u00b0C. Trace amounts of
contaminating DNA were removed from the RNA extracts using TURBO DNA
free\\u2122 (Invitrogen, USA) as directed by the manufacturer. To ensure DNA
was removed thoroughly, each RNA extract was treated twice with TURBO DNase
(Invitrogen, USA). A nested PCR reaction (2 x 35 cycles) using bacterial
primers was used to confirm the absence of DNA in our RNA solutions. RNA was
converted to cDNA using the Ovation\\u00ae RNA-Seq System V2 kit (NuGEN, USA)
according to the manufacturer\\u2019s protocol to preferentially prime non-rRNA
sequences.
The cDNA was purified with the MinElute Reaction Cleanup Kit (Qiagen, USA) and
eluted into 20 \\u03bcL elution buffer. Extracts were quantified using a Qubit
Fluorometer (Life Technologies, USA) and cDNAs were stored at -80 \\u00b0C
until sequencing using 150 bp paired-end Illumina NextSeq 550.
To control for potential contaminants introduced during drilling, sample
handling, and laboratory kit reagents, we sequenced a number of control
samples as above. Two samples controlled for potential nucleic acid
contamination; a \\u201cmethod\\u201d control to monitor possible contamination
from our laboratory extractions, which included ~ 40 g sterilized glass beads
processed through the entire protocol in place of rock, and a \\u201ckit\\u201d
control to account for any signal coming from trace contaminants in kit
reagents, which received no addition. In addition, 3 more controls were
extracted: a sample of the drilling mud (Sepiolite), and two drilling seawater
samples collected during the first and third weeks of drilling. cDNA obtained
from these controls were sequenced together with the rock samples and co-
assembled. Trimmomatic (v. 0.32) was used to trim adapter sequences
(leading=20, trailing=20, sliding window=04:24, minlen=50).
Paired reads were further quality checked and trimmed using FastQC (v. 0.11.7)
and FASTX-toolkit (v. 0.014). Downstream analyses utilized paired reads. After
co-assembling reads with Trinity (v. 2.4.0) from all controls (min length 150
bp), Bowtie2 (v. 2.3.4.1, 50) was used (with the parameter \\u2018un-
conc\\u2019) to align all sample reads to this co-assembly. Reads that mapped
to our control co-assembly allowing 1 mismatch were removed from further
analysis (23.5-68.5% of sequences remained in sample data sets, see
Supplementary Table 4). Trinity (v. 2.4.0) was used for de novo assembly of
the remaining reads in sample data sets (min. length 150 bp). Bowtie aligner
was used to align reads to assembled contigs, RSEM was used to estimate the
expression level of these reads, and TMM was used to perform cross sample
normalization and to generate a TMM-normalized expression matrix. Within the
Trinotate suite, TransDecoder (v. 3.0.1) was used to identify coding regions
within contigs and functional and taxonomic annotation was made 622 by BLASTx
and BLASTp against UniProt, Swissprot (release 2018_02) and RefSeq non-
redundant protein sequence (nr) databases (e-value threshold of 1e-5). BLASTp
was used to look for sequence homologies with the same e-values. HMMER (v.
3.1b2) was used to identify conserved domains by searching against the Pfam
(v31.0) database. SignalP (v. 4.1) and TMHMM (2.0c) were used to predict
signal peptides and transmembrane domains. RNAMMER (v.1.2) was used to
identify rRNA homologies of archaea, bacteria and eukaryotes.
Because the Swissprot database does not have extensive representation of
protein sequences from environmental samples, particularly deep-sea and deep
biosphere samples, annotations of contigs utilized for analyses of selected
processes were manually cross checked by BLASTx against GenBank nr database.
Aside from removing any reads that mapped well to our control co-assembly (1
mismatch), as an extra precaution, any sequence that exhibited \\u2265 95%
sequence identity over \\u2265 80% of the sequence length to suspected
contaminants (e.g., human pathogens, plants, or taxa known to be common
molecular kit reagent contaminants, and not described from the marine
environment) as in Salter et al. and Glassing et al. were removed.
This conservative approach potentially removed environmentally relevant data
that were annotated to suspected contaminants due to poor taxonomic
representation from environmental taxa in public databases, however it affords
the highest possible confidence about any transcripts discussed. Additional
functional annotations of contigs were obtained by BLAST against the KEGG,
COG, SEED, and MetaCyc databases using MetaPathways (v. 2.0) to gain insights
into particular cellularprocesses, and to provide overviews of metabolic
functions across samples based on comparisons of FPKM-normalized data. All
annotations were integrated into a SQLite database for further analysis.";
String awards_0_award_nid "709555";
String awards_0_award_number "OCE-1658031";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1658031";
String awards_0_funder_name "NSF Division of Ocean Sciences";
String awards_0_funding_acronym "NSF OCE";
String awards_0_funding_source_nid "355";
String awards_0_program_manager "David L. Garrison";
String awards_0_program_manager_nid "50534";
String cdm_data_type "Other";
String Conventions "COARDS, CF-1.6, ACDD-1.3";
String creator_email "info@bco-dmo.org";
String creator_name "BCO-DMO";
String creator_type "institution";
String creator_url "https://www.bco-dmo.org/";
String data_source "extract_data_as_tsv version 2.3 19 Dec 2019";
String dataset_current_state "Final and no updates";
String date_created "2020-05-26T20:31:21Z";
String date_modified "2020-07-08T20:46:44Z";
String defaultDataQuery "&time<now";
String history
"2024-11-23T17:07:32Z (local files)
2024-11-23T17:07:32Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_812936.html";
String infoUrl "https://www.bco-dmo.org/dataset/812936";
String institution "BCO-DMO";
String instruments_0_acronym "Automated Sequencer";
String instruments_0_dataset_instrument_description "RNA sequencing was performed using the Illumina NextSeq 550 platform (Univ. of Georgia).v";
String instruments_0_dataset_instrument_nid "813310";
String instruments_0_description "General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.";
String instruments_0_instrument_name "Automated DNA Sequencer";
String instruments_0_instrument_nid "649";
String instruments_0_supplied_name "Illumina NextSeq 550 platform";
String keywords "bco, bco-dmo, biological, biosynthetic, Biosynthetic_pathway, chemical, cycle, data, dataset, dmo, erddap, ID_19R1, ID_26R2, ID_2R1, ID_31R1, ID_42R2, ID_51R3, ID_62R1, ID_68R4, ID_71R1, ID_81R2, ID_84R6, management, oceanography, office, pathway, preliminary";
String license "https://www.bco-dmo.org/dataset/812936/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/812936";
String param_mapping "{'812936': {}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/812936/parameters";
String people_0_affiliation "Woods Hole Oceanographic Institution";
String people_0_affiliation_acronym "WHOI";
String people_0_person_name "Virginia P. Edgcomb";
String people_0_person_nid "51284";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "Woods Hole Oceanographic Institution";
String people_1_affiliation_acronym "WHOI";
String people_1_person_name "Virginia P. Edgcomb";
String people_1_person_nid "51284";
String people_1_role "Contact";
String people_1_role_type "related";
String people_2_affiliation "Woods Hole Oceanographic Institution";
String people_2_affiliation_acronym "WHOI BCO-DMO";
String people_2_person_name "Karen Soenen";
String people_2_person_nid "748773";
String people_2_role "BCO-DMO Data Manager";
String people_2_role_type "related";
String project "Subseafloor Lower Crust Microbiology";
String projects_0_acronym "Subseafloor Lower Crust Microbiology";
String projects_0_description
"NSF abstract:
The lower ocean crust has remained largely unexplored and represents one of the last frontiers for biological exploration on Earth. Preliminary data indicate an active subsurface biosphere in samples of the lower oceanic crust collected from Atlantis Bank in the SW Indian Ocean as deep as 790 m below the seafloor. Even if life exists in only a fraction of the habitable volume where temperatures permit and fluid flow can deliver carbon and energy sources, an active lower oceanic crust biosphere would have implications for deep carbon budgets and yield insights into microbiota that may have existed on early Earth. This is all of great interest to other research disciplines, educators, and students alike. A K-12 education program will capitalize on groundwork laid by outreach collaborator, A. Martinez, a 7th grade teacher in Eagle Pass, TX, who sailed as outreach expert on Drilling Expedition 360. Martinez works at a Title 1 school with ~98% Hispanic and ~2% Native American students and a high number of English Language Learners and migrants. Annual school visits occur during which the project investigators present hands on-activities introducing students to microbiology, and talks on marine microbiology, the project, and how to pursue science related careers. In addition, monthly Skype meetings with students and PIs update them on project progress. Students travel to the University of Texas Marine Science Institute annually, where they get a campus tour and a 3-hour cruise on the R/V Katy, during which they learn about and help with different oceanographic sampling approaches. The project partially supports two graduate students, a Woods Hole undergraduate summer student, the participation of multiple Texas A+M undergraduate students, and 3 principal investigators at two institutions, including one early career researcher who has not previously received NSF support of his own.
Given the dearth of knowledge of the lower oceanic crust, this project is poised to transform our understanding of life in this vast environment. The project assesses metabolic functions within all three domains of life in this crustal biosphere, with a focus on nutrient cycling and evaluation of connections to other deep marine microbial habitats. The lower ocean crust represents a potentially vast biosphere whose microbial constituents and the biogeochemical cycles they mediate are likely linked to deep ocean processes through faulting and subsurface fluid flow. Atlantis Bank represents a tectonic window that exposes lower oceanic crust directly at the seafloor. This enables seafloor drilling and research on an environment that can transform our understanding of connections between the deep subseafloor biosphere and the rest of the ocean. Preliminary analysis of recovered rocks from Expedition 360 suggests the interaction of seawater with the lower oceanic crust creates varied geochemical conditions capable of supporting diverse microbial life by providing nutrients and chemical energy. This project is the first interdisciplinary investigation of the microbiology of all 3 domains of life in basement samples that combines diversity and \"meta-omics\" analyses, analysis of nutrient addition experiments, high-throughput culturing and physiological analyses of isolates, including evaluation of their ability to utilize specific carbon sources, Raman spectroscopy, and lipid biomarker analyses. Comparative genomics are used to compare genes and pathways relevant to carbon cycling in these samples to data from published studies of other deep-sea environments. The collected samples present a rare and time-sensitive opportunity to gain detailed insights into microbial life, available carbon and energy sources for this life, and of dispersal of microbiota and connections in biogeochemical processes between the lower oceanic crust and the overlying aphotic water column.
About the study area:
The International Ocean Discovery Program (IODP) Expedition 360 explored the lower crust at Atlantis Bank, a 12 Ma oceanic core complex on the ultraslow-spreading SW Indian Ridge. This oceanic core complex represents a tectonic window that exposes lower oceanic crust and mantle directly at the seafloor, and the expedition provided an unprecedented opportunity to access this habitat in the Indian Ocean.";
String projects_0_end_date "2020-01";
String projects_0_geolocation "SW Indian Ridge, Indian Ocean";
String projects_0_name "Collaborative Research: Delineating The Microbial Diversity and Cross-domain Interactions in The Uncharted Subseafloor Lower Crust Using Meta-omics and Culturing Approaches";
String projects_0_project_nid "709556";
String projects_0_start_date "2017-02";
String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
String publisher_type "institution";
String sourceUrl "(local files)";
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "Supplementary Table 4A: Metatranscriptome data summary for cellular activities presented and statistics on sequencing and removal of potential contaminant sequences: FPKM values. Samples taken on board of the R/V JOIDES Resolution between November 30, 2015 and January 30, 2016.";
String title "[IODP360 - FPKM values] - Supplementary Table 4A: Metatranscriptome data summary for cellular activities presented and statistics on sequencing and removal of potential contaminant sequences, FPKM values (Collaborative Research: Delineating The Microbial Diversity and Cross-domain Interactions in The Uncharted Subseafloor Lower Crust Using Meta-omics and Culturing Approaches)";
String version "1";
String xml_source "osprey2erddap.update_xml() v1.5";
}
}
Data Access Protocol (DAP) and its
selection constraints.
The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.
Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names,
followed by a collection of
constraints (e.g., variable<value),
each preceded by '&' (which is interpreted as "AND").
For details, see the tabledap Documentation.